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Title: Data from "Signatures of a liquid-liquid transition in an ab initio deep neural network model for water"
Contributors: Gartner, Thomas III
Zhang, Linfeng
Piaggi, Pablo
Car, Roberto
Panagiotopoulos, Athanassios
Debenedetti, Pablo
Keywords: molecular simulation
machine learning
liquid-liquid transition
statistical mechanics
advanced sampling
Deep-potential molecular dynamics
Issue Date: Jul-2020
Abstract: This dataset contains all data related to the publication "Signatures of a liquid-liquid transition in an ab initio deep neural network model for water", by Gartner et al., 2020. In this work, we used neural networks to generate a computational model for water using high-accuracy quantum chemistry calculations. Then, we used advanced molecular simulations to demonstrate evidence that suggests this model exhibits a liquid-liquid transition, a phenomenon that can explain many of water's anomalous properties. This dataset contains links to all software used, all data generated as part of this work, as well as scripts to generate and analyze all data and generate the plots reported in the publication.
Description: The DNN model for water was generated using the DP-GEN methodology ( and DeepMD-kit v1.0 ( software. Simulations were performed using the LAMMPS molecular simulation software v7Aug19 ( patched with the Plumed 2.0 advanced sampling software ( Detailed methods are as described in Gartner et al, ( Detailed installation instructions for all software are available at the above linked webpages. Please download the data sets from Dropbox using
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Appears in Collections:Research Data Sets

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